Disclosed herein are system, method, and computer program product embodiments for configuring a dynamic reassignment of an application flow across different computation layers based on various conditions. An embodiment operates by assigning a first rule of an application flow to a first computation layer of a plurality of computation layers. The embodiment assigns a second rule of the application flow to a second computation layer of the plurality of computation layers. The embodiment assigns a transition rule of the application flow to the first computation layer. The transition rule includes an action that causes the first rule of the application flow to be executed in the second computation layer of the plurality of computation layers based on a condition. The embodiment then transmits the application flow to the plurality of computation layers thereby causing the application flow to be configured for execution.

Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

The disclosure provides an approach for distribution of functions among data centers of a cloud system that provides function-as-a-service (FaaS). For example, the disclosure provides one or more function distributors configured to receive a request for loading or executing a function, automatically determine an appropriate data center to load or execute the function, and automatically load or execute the function on the determined data center. In certain embodiments, the function distributors are further configured to determine an appropriate data center to provide storage resources for the function and configure the function to utilize the storage resources of the determined data center.

Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

Methods and systems for monitoring performance in a distributed storage system described. One example method includes identifying requests sent by clients to the distributed storage system, each request including request parameter values for request parameters; generating probe requests based on the identified requests, the probe requests including probe request parameter values for probe request parameter values, representing a statistical sample of the request parameters included in the identified requests; sending the generated probe requests to the distributed storage system over a network, wherein the distributed storage system is configured to perform preparations for servicing each probe request in response to receiving the probe request; receiving responses to the probe requests from the distributed storage system, and outputting at least one performance metric value measuring a current performance state of the distributed storage system based on the received responses.

An apparatus comprises an interrupt distributor to distribute virtual interrupts to one or more physical processors, each virtual interrupt to be handled by one of a plurality of virtual processors mappable to said one or more physical processors; and control circuitry to maintain virtual processor interrupt tracking information corresponding to a given virtual processor. The virtual processor interrupt tracking information includes a pending interrupt record tracking which types of virtual interrupts are pending for the given virtual processor, and separate from the pending interrupt record, a pending interrupt status indication indicating a pending interrupt status for the given virtual processor. The pending interrupt status indicates whether the number of pending virtual interrupts for the given virtual processor is zero.

The embodiments of the present invention enable novel methods, non-transitory mediums, and systems for encoding and generating haptic effects. According to the various embodiments, a media object is retrieved. The media object is analyzed to determine one or more time periods for rendering haptic effects. The haptic effects for rendering during the time periods are determined. The haptic effects are encoded as a haptic effect pattern that identifies a start time and duration for each of the haptic effects.

The embodiments of the present invention enable novel methods, non-transitory mediums, and systems for encoding and generating haptic effects. According to the various embodiments, a media object is retrieved. The media object is analyzed to determine one or more time periods for rendering haptic effects. The haptic effects for rendering during the time periods are determined. The haptic effects are encoded as a haptic effect pattern that identifies a start time and duration for each of the haptic effects.